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Difference between revisions of "Customer Data Management (CDM)"

 
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== Definition of Customer Data Management (CDM) ==
 
Customer Data Management (CDM) is a solution mechanism in which an organization's customer data is collected, managed and analyzed. CDM is geared toward resolving customer requirements and issues while enhancing customer retention and satisfaction, allowing an organization to convert customer data into Customer Intelligence (CI). With CDM, one or more software applications are integrated to facilitate access to reliable and efficient customer data. Attracting and retaining customers requires a clear understanding of customer requirements. CDM streamlines [[Customer Relationship Management (CRM)|customer relationship management (CRM)]], marketing and customer feedback management (CFM).<ref>Defining Customer Data Management (CDM) [https://www.techopedia.com/definition/28021/customer-data-management-cdm Techopedia]</ref>
 
Customer Data Management (CDM) is a solution mechanism in which an organization's customer data is collected, managed and analyzed. CDM is geared toward resolving customer requirements and issues while enhancing customer retention and satisfaction, allowing an organization to convert customer data into Customer Intelligence (CI). With CDM, one or more software applications are integrated to facilitate access to reliable and efficient customer data. Attracting and retaining customers requires a clear understanding of customer requirements. CDM streamlines [[Customer Relationship Management (CRM)|customer relationship management (CRM)]], marketing and customer feedback management (CFM).<ref>Defining Customer Data Management (CDM) [https://www.techopedia.com/definition/28021/customer-data-management-cdm Techopedia]</ref>
  
 
Customer data management is defined as the people, processes, technologies, and systems that collect, analyze, and organize customer data.
 
Customer data management is defined as the people, processes, technologies, and systems that collect, analyze, and organize customer data.
  
[[File:Customer_Data_Management.png|400px|Customer Data Management]]<br />
+
[[File:Customer_Data_Management.png|300px|Customer Data Management]]<br />
 
source: [https://www.martechadvisor.com/articles/data-management/customer-data-management-cdm-martech101/ MarTech Advisor]
 
source: [https://www.martechadvisor.com/articles/data-management/customer-data-management-cdm-martech101/ MarTech Advisor]
 +
 +
Centralizing the management of customer information is critical for a business’s continued longevity. According to DZone, 92 percent of organizations have 16 to 20 data sources, with that data spread across multiple locations in multiple formats. With so many data sources spread throughout the organization, and locked into functional and channel-specific silos, there is no way to build a single view of the customer without implementing CDM processes and technologies. Strong customer data management practices empower companies to build better products, orchestrate contextually relevant marketing campaigns, and provide a personalized customer experience. Customer retention dramatically improves in organizations with strong CDM practices, with Forbes Insights finding that data-driven marketing organizations are five times more likely to achieve a competitive advantage (74 percent vs. 13 percent). Higher customer retention is a tangible benefit to the organization. We all know that it’s cheaper to retain existing customers than acquire new ones, so the ability of CDM processes to improve that capability can provide substantial support to revenue. With customers increasingly demanding personalized experiences across channels, the benefits of robust CDM processes are hard to deny.<ref>Understanding Customer Data Management [https://www.redpointglobal.com/blog/what-is-customer-data-management/ Mike Ferguson]</ref>
 +
 +
 +
== Background of Customer Data Management (CDM)<ref>Background of Customer Data Management (CDM) [https://en.wikipedia.org/wiki/Customer_data_management Wikipedia]</ref> ==
 +
Customer data management, as a term, was coined in the 1990s, pre-dating the alternative term [[Enterprise Feedback Management (EFM)|enterprise feedback management (EFM)]]. CDM was introduced as a software solution that would replace earlier disc-based or paper-based surveys and spreadsheet data. Initially, CDM solutions were marketed to businesses as software, specific to one company, and often to one department within that company. This was superseded by [[Application Service Provider (ASP)|application service providers (ASPs)]] where software was hosted for end user organizations, thus avoiding the necessity for IT professionals to deploy and support [[Software|software]]. However, ASPs with their single-tenancy architecture were, in turn, superseded by [[Software as a Service (SaaS)|software as a service (SaaS)]], engineered for multi-tenancy. By 2007 SaaS applications, giving businesses on-demand access to their customer information, were rapidly gaining popularity compared with ASPs. Cloud computing now includes [[Software as a Service (SaaS)|SaaS]] and many prominent CDM providers offer cloud-based applications to their clients.
 +
 +
In recent years, there has been a push away from the term [[Enterprise Feedback Management (EFM)|EFM]], with many of those working in this area advocating the slightly updated use of CDM. The return to the term CDM is largely based on the greater need for clarity around the solutions offered by companies, and on the desire to retire terminology veering on techno-jargon that customers may have a hard time understanding.
 +
 +
 +
== Components of Customer Data Management (CDM)<ref>Components of Customer Data Management (CDM) [https://searchcustomerexperience.techtarget.com/definition/customer-data-management-CDM Techtarget]</ref> ==
 +
CDM must be tightly integrated across the departments of an organization, including IT, sales and HR. CDM components include:
 +
*Categorization: Customer data is classified and subclassified.
 +
*Correction: Collected data is verified for accuracy and consistency. When necessary, contact details are updated, and duplicate records are removed.
 +
*Enrichment: Incomplete data is collected and completed.
 +
*Collection: Customer data and insight activity is collected via a customer feedback system or sources, like sales, customer support, surveys, reports, newsletters and other customer interactions.
 +
Customer data is organized and shared throughout an organization.
 +
 +
 +
[[File:Components_of_CDM.png|500px|Components of Customer Data Management]]<br />
 +
source: Techtarget
 +
 +
 +
Organizations can implement CDM with in-house software tools or [[Cloud Computing|cloud computing]] services that collect, analyze and organize customer information in a single, consistent platform. Once in place, the [[Data Access|data can be accessed]] in real time by all relevant departments across the entire [[Organization|organization]], including sales, [[Marketing|marketing]] and customer support. CDM software products can be used in a variety of ways, such as:
 +
*Allow [[Stakeholder|stakeholders]] to initiate an instant response to customer feedback or issues.
 +
*Allow stakeholders to identify and contact a target audience segment
 +
*Allow stakeholders to identify and contact specific marketing qualified leads (MQLs) and sales qualified leads (SQLs).
 +
 +
 +
== Customer Data Management Strategy<ref>Building Your Customer Data Management Strategy [https://www.martechadvisor.com/articles/data-management/how-to-plan-your-customer-data-management-strategy/ MTA]</ref> ==
 +
Customer data is the most valuable resource of a business. Customer data management includes collecting, cleaning, managing, tracking, analyzing, and combining customer data to glean insights for predicting customer preferences, sales trends, etc. Below are five basic steps to help you plan an effective customer data strategy:
 +
*Identify Critical Data: According to a Forrester blog post, “On average, between 60% and 73% of all data within an enterprise goes unused for analytics.” What effects can unused data have on your systems?
 +
**Too much data can become unwieldy.
 +
**It can hinder the decision-making process, as it responds much slower to manipulation and retrieval.
 +
**Unused data becomes an unnecessary burden on your system.
 +
Identifying critical data, thus becomes a pivotal step in customer data management. You need to strategize what and how much data to collect.
 +
*Decide on Data Collection Methods: After identifying what critical data you need, decide where and how to collect it. Some important considerations while collecting data are:
 +
**Identifying direct sources — surveys, web forms, contests, purchase history and customer records in your CRM system
 +
**Including indirect methods — customer activity on your web portal and social media pages or building personalized profiles based on a person’s location and behavior patterns.
 +
**Being ethical and upfront while collecting data. Trust is crucial for building relationships with your customers. Be clear about what data you are collecting, how you will use it and request customer consent.
 +
*Maintain Data Well: Data becomes unusable if it is not sorted and verified. Opportunities are lost and resources are wasted if it is not updated regularly. Data maintenance involves setting up a data management team who will:
 +
**Integrate data from marketing, sales, and service teams
 +
**Validate whether the customer information is correct
 +
**Verify if the information is consistent
 +
**Clean the database by eliminating redundancies, duplicates, incomplete or erroneous records
 +
**Update the database regularly to record customer dynamics
 +
**Maintain overall data quality
 +
*Invest in the Right Technology: Spreadsheets are no longer enough to manage, analyze, and efficiently retrieve data. You need to invest in the right technology to better manage and access data, eliminate silos, and provide real-time insights. Marketers’ favorite solutions include:
 +
**Customer Data Platforms (CDPs): CDPs can ingest any form of data from disparate sources, handle varying volumes of customer information, and unify customer records to make them readily accessible.
 +
**Customer Relationship Management (CRM): CRM systems can help integrate customer data and interactions across touchpoints at one place. They can segment customer data to glean insights, and track the customer journey to identify opportunities.
 +
*Focus on Customer Data Security: Maintaining customer privacy and ensuring that customer data is secure is paramount. It helps build customer trust, saves your business from losses, and prevents litigation. The security measures you can undertake are:
 +
**Invest in backup systems
 +
**Devise robust security policies that include password policy, encryption, biometric authentication, etc.
 +
**Invest in security training for your employees
 +
An effective customer data management strategy includes responsible data collection and management, securing better quality data and focusing on [[Data Security|data security]].
 +
 +
 +
== Customer Data Management Best Practices<ref>7 Best Practices For Customer Data Management [https://www.datapine.com/blog/customer-data-management-best-practices/ Datapine]</ref> ==
 +
It is clear that effective customer data management has the potential to maximize your business’s potential in a number of ways. To help steer your ongoing success, here are 7 customer data management best practices:
 +
*Invest in training for your employees: An employee that actively applies data analysis practices to their work can be as much as ten times more productive than someone with little or no practical experience in [[Data Analysis|data analysis]]. The pivotal element that sets an experienced data analyst apart from a novice is the ability to understand the concept data on a comprehensive level, including the creation of a complete analytical report. This understanding yields a wealth of new resources and insights that can be used to enrich the business’s overall data-centric strategies. Invest in training for your business and IT staff – with their buy-in (and newfound skills), you’ll be able to conduct truly effective [[Data Management|data management]]. Smaller businesses should also consider the wealth of economical training options available today. The Internet makes it entirely possible to learn analysis through data analysis books and online courses, many of which are accessible at a low cost or free.
 +
*Use validation tools: Customer databases consist of millions of records, and each customer is equipped with their own address details, such as the zip code and other invaluable information. Records that fail to include this level of data can cause real problems in the communication process. When this situation occurs, address verification or validation tools can become incredibly useful – and it’s possible to integrate them with almost any leading verification software with popular [[Customer Relationship Management (CRM)|CRM]] or [[Enterprise Relationship Management (ERM)|ERM]] systems.
 +
*Appoint data control: Concerning client database management, your analysis should be divided into departments where only a handful of people have full administrative privileges. Unless you take the necessary precautions, you run the risk of having to deal with multiple non-common data entries that may make your stats, facts, figures, and metrics inconsistent. A good example would be acronyms for countries – you may input ‘US’ into your system, but someone else might have inserted ‘United States’. Such inconsistencies can have a huge effect on the way data is organized through a host of different management systems within a company. The best way to tackle this problem is to assign a special customer management group that knows all the necessary rules that govern your internal customer data creation. With the help of a decision support software, you may also expand this further into departments so that everyone is up-to-date on the right approach to customer data management.
 +
*Monitor your data: Customer data is a state of constant flux, which is the number one reason to employ solid data monitoring principles. You may want to use specific notification techniques to maintain overall [[Data Quality|data quality]] and establish specific security policies that keep data organized and on point. A bi-weekly scan of incomplete or erroneous records is essential to keep your database fully optimized and updated. Moreover, twice a week, you should also check your data for any unnecessary records and entries that should be cleaned – an essential component of client database management success. An online report generator can decrease the amount of time needed for these kinds of tasks and increase the quality of the [[Data Monitoring|data monitoring]] processes. With concrete data monitoring principles, you are well prepared to get all your key metrics out of your data with a smart [[Key Performance Indicator (KPI)|KPI]] software.
 +
*Focus on relevant data for relevant results: It is easy to get sidetracked with customer data management and optimize the particular CRM system in such a way that every available source of data is being tracked constantly. But sometimes, you will only need the very surface of all our available consumer data to make the most sound decisions for your company. It can be overwhelming for anyone in the sales department to come across a plethora of data choices when the only thing that’s really necessary is the understanding of which particular data set is most important for the business at any given time. To avoid these ‘data overload’ roadblocks, focus on implementing your sales report methods in a way that strives for better informational quality over data quantity – a priceless customer data management strategy.
 +
*Avoid data fragmentation where possible: When it comes to customer data management strategy, in addition to selecting your consumer data wisely (mining for quality over quantity), implementing a cohesive information collection process is essential. While you might get the vast majority of your customer insights from a small handful of tools, platforms, or sources, without the ability to view, interact, and analyze with your data from one central location, your information could become fragmented, making it less effective. It is worth investing in customer data management software that allows you to collect, curate, and drill down into your consumer insights from one central location or live dashboard. That way, you will ensure cohesion and fluidity as well as a full and reliable view of the information that is most valuable to your business.
 +
*Visualize your data: 90% of the information transmitted to our brains is visual. Moreover, those who follow directions with illustrations thrive 323% more than people who follow text-only directions. By [[Data Visualization|visualizing your customer data]] with the help of an online data visualization tool, aiming of using it to drill home an important set of insights or tell a story, you will make this all-important information widely accessible across the business. If people can understand the data before them because it’s more visually digestible, they will be able to develop initiatives that will ultimately enhance the level of customer experience you offer your audience, resulting in increased commercial success.
 +
 +
 +
== See Also ==
 +
[[Customer]]<br />
 +
[[Customer Acquisition Cost (CAC)]]<br />
 +
[[Customer Centricity]]<br />
 +
[[Customer Demographics]]<br />
 +
[[Customer Due Diligence (CDD)]]<br />
 +
[[Customer Dynamics]]<br />
 +
[[Customer Effort Score (CES)]]<br />
 +
[[Customer Engagement]]<br />
 +
[[Customer Engagement Hub (CEH)]]<br />
 +
[[Customer Experience Management (CEM)]]<br />
 +
[[Customer Lifecycle]]<br />
 +
[[Customer Lifetime Value]]<br />
 +
[[Customer Loyalty]]<br />
 +
[[Customer Needs]]<br />
 +
[[Customer Retention]]<br />
 +
[[Customer Service]]<br />
 +
[[Customer Service Management]]<br />
 +
[[Customer Relationship Management (CRM)]]<br />
 +
[[Customer Data Integration (CDI)]]<br />
 +
[[Enterprise Data Integration (EDI)]]<br />
 +
[[Data Management]]
 +
 +
 +
== References ==
 +
<references/>
 +
 +
 +
== Further Reading ==
 +
*Customer Data Management: What Really Matters [https://www.chiefmarketer.com/customer-data-management-what-really-matters/ Chief Marketer]
 +
*Customer Data Management: How to Gather Customer Data Correctly [https://exponea.com/blog/customer-data-management/ Exponea]
 +
*5 best practices for customer data management [https://www.godaddy.com/garage/5-best-practices-for-customer-data-management/ Erik Deckers]
 +
*Customer Data Management & Operations Best Practices In 2019 [https://www.hull.io/blog/customer-data-management/ Ed Fry]
 +
*Customer Data Management: 20 Experts Reveal Their Top Tips to Effectively Manage Customer Data [https://www.ngdata.com/customer-data-management-best-practices/ NGData]

Latest revision as of 12:34, 11 October 2019

Definition of Customer Data Management (CDM)

Customer Data Management (CDM) is a solution mechanism in which an organization's customer data is collected, managed and analyzed. CDM is geared toward resolving customer requirements and issues while enhancing customer retention and satisfaction, allowing an organization to convert customer data into Customer Intelligence (CI). With CDM, one or more software applications are integrated to facilitate access to reliable and efficient customer data. Attracting and retaining customers requires a clear understanding of customer requirements. CDM streamlines customer relationship management (CRM), marketing and customer feedback management (CFM).[1]

Customer data management is defined as the people, processes, technologies, and systems that collect, analyze, and organize customer data.

Customer Data Management
source: MarTech Advisor

Centralizing the management of customer information is critical for a business’s continued longevity. According to DZone, 92 percent of organizations have 16 to 20 data sources, with that data spread across multiple locations in multiple formats. With so many data sources spread throughout the organization, and locked into functional and channel-specific silos, there is no way to build a single view of the customer without implementing CDM processes and technologies. Strong customer data management practices empower companies to build better products, orchestrate contextually relevant marketing campaigns, and provide a personalized customer experience. Customer retention dramatically improves in organizations with strong CDM practices, with Forbes Insights finding that data-driven marketing organizations are five times more likely to achieve a competitive advantage (74 percent vs. 13 percent). Higher customer retention is a tangible benefit to the organization. We all know that it’s cheaper to retain existing customers than acquire new ones, so the ability of CDM processes to improve that capability can provide substantial support to revenue. With customers increasingly demanding personalized experiences across channels, the benefits of robust CDM processes are hard to deny.[2]


Background of Customer Data Management (CDM)[3]

Customer data management, as a term, was coined in the 1990s, pre-dating the alternative term enterprise feedback management (EFM). CDM was introduced as a software solution that would replace earlier disc-based or paper-based surveys and spreadsheet data. Initially, CDM solutions were marketed to businesses as software, specific to one company, and often to one department within that company. This was superseded by application service providers (ASPs) where software was hosted for end user organizations, thus avoiding the necessity for IT professionals to deploy and support software. However, ASPs with their single-tenancy architecture were, in turn, superseded by software as a service (SaaS), engineered for multi-tenancy. By 2007 SaaS applications, giving businesses on-demand access to their customer information, were rapidly gaining popularity compared with ASPs. Cloud computing now includes SaaS and many prominent CDM providers offer cloud-based applications to their clients.

In recent years, there has been a push away from the term EFM, with many of those working in this area advocating the slightly updated use of CDM. The return to the term CDM is largely based on the greater need for clarity around the solutions offered by companies, and on the desire to retire terminology veering on techno-jargon that customers may have a hard time understanding.


Components of Customer Data Management (CDM)[4]

CDM must be tightly integrated across the departments of an organization, including IT, sales and HR. CDM components include:

  • Categorization: Customer data is classified and subclassified.
  • Correction: Collected data is verified for accuracy and consistency. When necessary, contact details are updated, and duplicate records are removed.
  • Enrichment: Incomplete data is collected and completed.
  • Collection: Customer data and insight activity is collected via a customer feedback system or sources, like sales, customer support, surveys, reports, newsletters and other customer interactions.

Customer data is organized and shared throughout an organization.


Components of Customer Data Management
source: Techtarget


Organizations can implement CDM with in-house software tools or cloud computing services that collect, analyze and organize customer information in a single, consistent platform. Once in place, the data can be accessed in real time by all relevant departments across the entire organization, including sales, marketing and customer support. CDM software products can be used in a variety of ways, such as:

  • Allow stakeholders to initiate an instant response to customer feedback or issues.
  • Allow stakeholders to identify and contact a target audience segment
  • Allow stakeholders to identify and contact specific marketing qualified leads (MQLs) and sales qualified leads (SQLs).


Customer Data Management Strategy[5]

Customer data is the most valuable resource of a business. Customer data management includes collecting, cleaning, managing, tracking, analyzing, and combining customer data to glean insights for predicting customer preferences, sales trends, etc. Below are five basic steps to help you plan an effective customer data strategy:

  • Identify Critical Data: According to a Forrester blog post, “On average, between 60% and 73% of all data within an enterprise goes unused for analytics.” What effects can unused data have on your systems?
    • Too much data can become unwieldy.
    • It can hinder the decision-making process, as it responds much slower to manipulation and retrieval.
    • Unused data becomes an unnecessary burden on your system.

Identifying critical data, thus becomes a pivotal step in customer data management. You need to strategize what and how much data to collect.

  • Decide on Data Collection Methods: After identifying what critical data you need, decide where and how to collect it. Some important considerations while collecting data are:
    • Identifying direct sources — surveys, web forms, contests, purchase history and customer records in your CRM system
    • Including indirect methods — customer activity on your web portal and social media pages or building personalized profiles based on a person’s location and behavior patterns.
    • Being ethical and upfront while collecting data. Trust is crucial for building relationships with your customers. Be clear about what data you are collecting, how you will use it and request customer consent.
  • Maintain Data Well: Data becomes unusable if it is not sorted and verified. Opportunities are lost and resources are wasted if it is not updated regularly. Data maintenance involves setting up a data management team who will:
    • Integrate data from marketing, sales, and service teams
    • Validate whether the customer information is correct
    • Verify if the information is consistent
    • Clean the database by eliminating redundancies, duplicates, incomplete or erroneous records
    • Update the database regularly to record customer dynamics
    • Maintain overall data quality
  • Invest in the Right Technology: Spreadsheets are no longer enough to manage, analyze, and efficiently retrieve data. You need to invest in the right technology to better manage and access data, eliminate silos, and provide real-time insights. Marketers’ favorite solutions include:
    • Customer Data Platforms (CDPs): CDPs can ingest any form of data from disparate sources, handle varying volumes of customer information, and unify customer records to make them readily accessible.
    • Customer Relationship Management (CRM): CRM systems can help integrate customer data and interactions across touchpoints at one place. They can segment customer data to glean insights, and track the customer journey to identify opportunities.
  • Focus on Customer Data Security: Maintaining customer privacy and ensuring that customer data is secure is paramount. It helps build customer trust, saves your business from losses, and prevents litigation. The security measures you can undertake are:
    • Invest in backup systems
    • Devise robust security policies that include password policy, encryption, biometric authentication, etc.
    • Invest in security training for your employees

An effective customer data management strategy includes responsible data collection and management, securing better quality data and focusing on data security.


Customer Data Management Best Practices[6]

It is clear that effective customer data management has the potential to maximize your business’s potential in a number of ways. To help steer your ongoing success, here are 7 customer data management best practices:

  • Invest in training for your employees: An employee that actively applies data analysis practices to their work can be as much as ten times more productive than someone with little or no practical experience in data analysis. The pivotal element that sets an experienced data analyst apart from a novice is the ability to understand the concept data on a comprehensive level, including the creation of a complete analytical report. This understanding yields a wealth of new resources and insights that can be used to enrich the business’s overall data-centric strategies. Invest in training for your business and IT staff – with their buy-in (and newfound skills), you’ll be able to conduct truly effective data management. Smaller businesses should also consider the wealth of economical training options available today. The Internet makes it entirely possible to learn analysis through data analysis books and online courses, many of which are accessible at a low cost or free.
  • Use validation tools: Customer databases consist of millions of records, and each customer is equipped with their own address details, such as the zip code and other invaluable information. Records that fail to include this level of data can cause real problems in the communication process. When this situation occurs, address verification or validation tools can become incredibly useful – and it’s possible to integrate them with almost any leading verification software with popular CRM or ERM systems.
  • Appoint data control: Concerning client database management, your analysis should be divided into departments where only a handful of people have full administrative privileges. Unless you take the necessary precautions, you run the risk of having to deal with multiple non-common data entries that may make your stats, facts, figures, and metrics inconsistent. A good example would be acronyms for countries – you may input ‘US’ into your system, but someone else might have inserted ‘United States’. Such inconsistencies can have a huge effect on the way data is organized through a host of different management systems within a company. The best way to tackle this problem is to assign a special customer management group that knows all the necessary rules that govern your internal customer data creation. With the help of a decision support software, you may also expand this further into departments so that everyone is up-to-date on the right approach to customer data management.
  • Monitor your data: Customer data is a state of constant flux, which is the number one reason to employ solid data monitoring principles. You may want to use specific notification techniques to maintain overall data quality and establish specific security policies that keep data organized and on point. A bi-weekly scan of incomplete or erroneous records is essential to keep your database fully optimized and updated. Moreover, twice a week, you should also check your data for any unnecessary records and entries that should be cleaned – an essential component of client database management success. An online report generator can decrease the amount of time needed for these kinds of tasks and increase the quality of the data monitoring processes. With concrete data monitoring principles, you are well prepared to get all your key metrics out of your data with a smart KPI software.
  • Focus on relevant data for relevant results: It is easy to get sidetracked with customer data management and optimize the particular CRM system in such a way that every available source of data is being tracked constantly. But sometimes, you will only need the very surface of all our available consumer data to make the most sound decisions for your company. It can be overwhelming for anyone in the sales department to come across a plethora of data choices when the only thing that’s really necessary is the understanding of which particular data set is most important for the business at any given time. To avoid these ‘data overload’ roadblocks, focus on implementing your sales report methods in a way that strives for better informational quality over data quantity – a priceless customer data management strategy.
  • Avoid data fragmentation where possible: When it comes to customer data management strategy, in addition to selecting your consumer data wisely (mining for quality over quantity), implementing a cohesive information collection process is essential. While you might get the vast majority of your customer insights from a small handful of tools, platforms, or sources, without the ability to view, interact, and analyze with your data from one central location, your information could become fragmented, making it less effective. It is worth investing in customer data management software that allows you to collect, curate, and drill down into your consumer insights from one central location or live dashboard. That way, you will ensure cohesion and fluidity as well as a full and reliable view of the information that is most valuable to your business.
  • Visualize your data: 90% of the information transmitted to our brains is visual. Moreover, those who follow directions with illustrations thrive 323% more than people who follow text-only directions. By visualizing your customer data with the help of an online data visualization tool, aiming of using it to drill home an important set of insights or tell a story, you will make this all-important information widely accessible across the business. If people can understand the data before them because it’s more visually digestible, they will be able to develop initiatives that will ultimately enhance the level of customer experience you offer your audience, resulting in increased commercial success.


See Also

Customer
Customer Acquisition Cost (CAC)
Customer Centricity
Customer Demographics
Customer Due Diligence (CDD)
Customer Dynamics
Customer Effort Score (CES)
Customer Engagement
Customer Engagement Hub (CEH)
Customer Experience Management (CEM)
Customer Lifecycle
Customer Lifetime Value
Customer Loyalty
Customer Needs
Customer Retention
Customer Service
Customer Service Management
Customer Relationship Management (CRM)
Customer Data Integration (CDI)
Enterprise Data Integration (EDI)
Data Management


References

  1. Defining Customer Data Management (CDM) Techopedia
  2. Understanding Customer Data Management Mike Ferguson
  3. Background of Customer Data Management (CDM) Wikipedia
  4. Components of Customer Data Management (CDM) Techtarget
  5. Building Your Customer Data Management Strategy MTA
  6. 7 Best Practices For Customer Data Management Datapine


Further Reading

  • Customer Data Management: What Really Matters Chief Marketer
  • Customer Data Management: How to Gather Customer Data Correctly Exponea
  • 5 best practices for customer data management Erik Deckers
  • Customer Data Management & Operations Best Practices In 2019 Ed Fry
  • Customer Data Management: 20 Experts Reveal Their Top Tips to Effectively Manage Customer Data NGData